Method and apparatus for analyzing biological tissue specimens

ABSTRACT

The present invention relates to a method and an apparatus for processing images of irregularly shaped objects, such as biological specimens, in particular of human or animal origin, or images thereof. The metric quantification of a biological body part or tissue or of an abnormal material spot or aggregate contained therein is also performed by means of the invention method. In particular, the present invention relates to a method for processing images of irregularly shaped objects, comprising a stage of acquisition of a digital image of said object, a stage of image elaboration (IMAEL) for quantifying said digital image to 1 bit and a stage of metrical processing of said 1-bit quantized image, wherein said stage of metrical processing comprises: -) a stage of object&#39;s metrical quantification (QUANT) for determining Euclidean perimeter P and/or area A of said object; -) a stage of dimensional calculation (DIM-CLC) for calculating a fractal-corrected perimeter Pf and/or area Af of said object.

The present invention relates to a method and an apparatus forprocessing images of irregularly shaped objects, such as biologicalspecimens, in particular of human or animal origin, or images thereof.The metric quantification of a biological body part or tissue or of anabnormal material spot or aggregate contained therein is also performedby means of the invention method.

With the term “abnormal material spot or aggregate” it is intended amaterial spot or aggregate morphologically connected with a pathologicalcondition or a condition which gives rise to a pre- or post-pathologicalsituation. Examples of abnormal material spot or aggregate may betumors, atherosclerotic plaques, edemas, hematomes, acute or chronicinflammatory lesions, scars and collagen diseases.

Observation and analysis of human, animal or plant tissues is normallyperformed by means of a microscope. Workstations are known in which amicroscope is operatively connected with a camera or video foracquisition of an image and with a computer for visual analyisis andelaboration of the acquired image.

On the other hand, when the diagnosis of a pathology requires theobservation of a body part or organ, such observation can be direct orthrough indirect means, such as radiography, Computerised AxialTomography (CAT), ecography analysis and the like. Again, an image, i.e.a digital image of the observed body part or organ can be acquired andanalysed by means of the computer alone or of the computer/camerasystem.

In any case, several drawbacks are however present in the knownapparatuses. The main drawback concerns the way the acquired image isprocessed by the computer. It is in fact necessary, in some cases, toevaluate physical and geometrical characteristics of the observed bodypart or of the abnormal material spot or aggregate, in order to assessthe evolution of the pathology. A typical example is the evaluation ofthe extension of atherosclerotic plaques or of tumors. In such a case,the known devices do not allow a correct quantification of the requestedparameters (perimeter, area, etc.) to be made, particularly for highlyirregularly shaped objects such as the ones named above, with theconsequence that the outcome of the analysis may be incorrect or evenmisleading. There is therefore a need of improved apparatuses that allowa correct quantification of the morphometric parameters of any item forwhich such quantification is requested.

Another typical problem in the case of observation through microscope isthe automatic focusing of the image. In fact, when at high magnificationa scanning of the slide is required in order to observe or capture theoverall image to be analysed, a fast and accurate focusing of eachscanned part is difficult to be performed.

The present invention addresses the above and other problems and solvethem with a method and an apparatus as depicted in the attached claims.

Further characteristics and the advantages of the method and apparatusfor analyizing irregularly shaped objects' images according to thepresent invention will become clear from the following description of apreferred embodiment thereof, given by way of non-limiting example, withreference to the appended drawings, in which:

FIG. 1 is a schematic view of the apparatus according to the invention;

FIG. 2 is a flow chart illustrating the method of acquiring an imageaccording to the invention;

FIG. 3 is a flow chart illustrating the method of processing theacquired image according to the invention.

The method of the invention allows one to analyse and metricallyquantify an object's image, particularly the image of an object havingirregular contour, whose Euclidean dimensions are not representative ofthe actual dimensions of the object. Such a kind of objects recur oftenwhen analyising a biological specimen. However, the method of theinvention should not be intended as limited to such a particular field,but can be validly employed in any field of application wherein it isnecessary to analyse, whether through microscope observation or bydirect image observation, an irregular object's image, such as in thecase of a topographical or geophysical survey.

With the term “biological specimens” it is herein intended any kind ofbiological sample taken from the human, animal or plant body (such as atissue or cell sample) or any image of a human, animal or plant bodypart (such as a radiography, ecography, CAT and the like).

The example that will be described hereinafter concerns a system 1 foracquiring and processing an image comprising a microscope 2 having amotorised scanning stage 3 capable of moving along the Cartesian axis x,y, z. The microscope 2 is preferably of the type that allowmagnification of from 50× up to 1000×.

The microscope 2 is provided with at least one object glass 8, at leastone eyepiece 4 and at least one photo-video port 5 for cameraattachment. To this latter, electronic image acquisition means 6, inparticular a photo/video camera, are operatively connected. Preferably,such electronic image acquisition means 6 are a digital camera, havingmore preferably a resolution of 1.3 Megapixels.

The electronic image acquisition means 6 are operatively connected witha processing system 7. The processing system 7 may be realized by meansof a personal computer (PC) comprising a bus which interconnects aprocessing means, for example a central processing unit (CPU), tostoring means, including, for example, a RAM working memory, a read-onlymemory (ROM)—which includes a basic program for starting the computer—,a magnetic hard disk, optionally a drive (DRV) for reading optical disks(CD-ROMs), optionally a drive for reading/writing floppy disks.Moreover, the processing system 7 optionally comprises a MODEM or othernetwork means for controlling communication with a telematics network, akeyboard controller, a mouse controller and a video controller. Akeyboard, a mouse and a monitor 8 are connected to the respectivecontrollers. The electronic image acquisition means 6 are connected tothe bus by means of an interface port (ITF). The scanning stage 3 isalso connected to the bus by means of a control interface port (CITF) bywhich the movement of the stage along the Cartesian axis is governed.

A program (PRG), which is loaded into the working memory during theexecution stage, and a respective data base are stored on the hard disk.Typically, the program (PRG) is distributed on one or more CD-ROMs forthe installation on the hard disk.

Similar considerations apply if the processing system 7 has a differentstructure, for example, if it is constituted by a central unit to whichvarious terminals are connected, or by a telematic computer network(such as Internet, Intranet, VPN), if it has other units (such as aprinter), etc. Alternatively, the program is supplied on floppy disk, ispre-loaded onto the hard disk, or is stored on any other substrate whichcan be read by a computer, is sent to a user's computer by means of thetelematics network, is broadcast by radio or, more generally, issupplied in any form which can be loaded directly into the workingmemory of the user's computer.

Coming now to the description of the method for acquiring and processingan image of a biological specimen according to the invention, thespecimen slide is placed on the scanning stage 3 of the microscope 2.

It is pointed out that some of the steps of the method of the inventioncan be performed by the computer system 7 by executing the program PRG.

The first stage of the method of the invention is the stage ofidentification of the object whose image should be acquired andquantified. (ID stage).

The following method for identifying the object of interest is based onthe assumption that such an object is clearly identifiable due to thehigh contrast of brightness between the object and the background. Ifsuch a high contrast is not originally present in the specimen underobservation, it can be enhanced for example by staining the specimenwith a suitable stain that marks the object or the background.

At the beginning of the ID stage, the magnification is set at the wantedvalue, in the example 200× magnification. The method starts by:

1a) generating a grid formed by a plurality of boxes to overlap on theimage; then,

2a) sending a command by the CPU to the motorized scanning stage 3 toposition on the axis x, y in a first position (start position)corresponding to the alignment position of the microscope's object glasswith a first box of the grid whose image has to be acquired and acommand to the electronic image acquisition means 6 for acquiring thedigital image for such a first box, the image being temporarily saved inthe RAM memory. Once the image of one box has been acquired,

3a) evaluating by the CPU the brightness of the single pixels in thefirst box, comparing it with a preset value and determining thebrightness contrast inside the box. The method then goes on by

4a) sending a command to the motorized scanning stage 3 to position onthe axis x, y in a next position (second position) corresponding to asecond box of the grid, sending a command to the electronic imageacquisition means 6 for acquiring and temporarily saving on the RAMmemory the digital image for such a second box and repeating theoperations of step 3) on such image.

The method is continued by

5a) reiterating the routine of step 4) until the whole slide is scannedand the images for each box of the grid are processed. During theexecution of the whole routine, the x, y position of the boxes of thegrid having a brightness contrast above a predefined value are saved inthe hard-disk memory.

Preferably, step 3a) of processing the image of the box of the gridwhich has been temporarily saved in the RAM memory is performedaccording to the following method:

i) building a histogram of the brightness intensities of the pixels ofthe analysed box,

ii) calculating the standard deviation from the mean value of thehistogram, and

iii) comparing the calculated value of standard deviation with apredefined value.

The position of the boxes having a standard deviation above such apredefined value is saved on the hard disk. Such a predefined value ofstandard deviation will depend upon the kind of object that should bedetected, which on its turn depends on the kind of histological tissue,how it is stained, etc.

The procedure described at points i), ii) and iii) is not the onlypossible for performing step 3), other known methods being usable, buthas the advantage of allowing a reliable result even in the case of ablurred image. It is in fact to be remarked here that, at this stage,focusing of the image has not been usually made yet. Focusing beforehaving identified the object to be observed would result in unacceptableincrease of the time spent for the whole procedure.

More preferably, the above ID stage is replaced by or preceded by anidentification preview stage (ID-PREV stage) in which the same steps 1)to 5) are performed, but by setting the microscope 2 at a lowermagnification (for example, 25× to 110×). This procedure allows a fasterexecution of the ID stage, since the number of boxes of the grid toiterate will be less. In the case the ID-PREV stage precedes but doesnot replace the ID stage, this latter will be performed only on the areaof the overall image in which the presence of the object has beendetected by the ID-PREV.

It should be understood that the object identification stage abovedescribed is not strictly necessary for the performance of the method ofthe invention, even if it allows automatization of the method and afaster execution thereof. In absence of the ID stage and/or ID-PREVstage, identification of the sample can be made manually. On the otherhand, absence of a even manual identification stage would cause thefurther image acquisition stage too long, since the whole slide's imagewould be captured.

The second stage of the method of the invention is the stage of focussetting (FCS stage). According to this second stage:

1b) a plurality of focus points is selected on the object image to beacquired;

2b) the CPU sends a command to the scanning stage 3 to position thefirst focus point below the microscope's object glass;

3b) said first focus point is manually brought into focus and its focusparameters are automatically saved in the storing means of theprocessing system 7;

4b) the routine of steps 2b) and 3b) is repeated for each point.

The selected focus points in step 1b) may be equally spaced apart fromeach other or in any case homogenously distributed on the object'ssurface. More preferably, nine focus points are selected and are locatedat the four corners of the largest parallelepiped inscribed into theobject under examination, at the center thereof and at the median pointof the parallelepiped's sides.

The third stage of the method is the stage of white calibration (WCALstage). This stage is performed by

1c) acquiring through the electronic image acquisition means 6 the imageof a specimen-free region of the slide (blank image) and saving it inthe storing means of the processing system 7.

This blank image will be subtracted by the acquired image of each imageregion in the subsequent image acquisition stage. This will allow toeliminate any borderline shadow effect in the acquired images.

The fourth stage of the method of the invention is the stage of imageacquisition (IM-ACQ stage), which is accomplished according to thefollowing steps:

1d) the CPU sends a command to the scanning stage 3 in order to move itto a first saved box′ position of the grid, selected according toprevious step 5a), in alignment with the microscope's object glass;

2d) calculating the focus parameters for said first box image byinterpolation from the focus parameters calculated according to previoussteps 1b) to 4b) for at least two focus points proximal to the saidfirst box;

3d) acquiring the image of said first box through said image acquisitionmeans 6;

4d) subtracting from the acquired image of said first box the blankimage acquired according to step 1c) above;

5d) saving the image resulting from step 4d) in the storing means of theprocessing system 7;

6d) repeating steps from 1d) to 5d) until the whole object to beacquired has been scanned;

7d) reassembling the whole image of the object by aligning the images ofthe single boxes in relation to their initial position and saving saidwhole image in the storing means of the processing system 7.

Preferably, said step 7d) of reassembling the whole image of the objectis accomplished by:

l) aligning each box′ image with the adjacent box′ image by overlappingthe edges of the image's side in the direction of alignment;

m) in the region of overlap, minimizing the difference of brightnessand/or colour intensity between overlapping pixels by shifting the box′images one with respect to each other;

n) repeating steps l) and m) for each pair of adjacent boxes.

The next stage of the method of the invention is the stage of imageelaboration (IMA-EL stage). This stage is performed by quantizing theimage to “1 bit” in order to select image's regions on which furthercalculations are performed. The IMA-EL stage is accomplished accordingto the following steps:

1e) considering a parameter for each pixel;

2e) comparing said pixel's parameter with a preset threshold value orthreshold range for said parameter;

3e) selecting a cluster of active pixels and a cluster of inactivepixels on the base of said comparison.

Said pixel's parameter is preferably brightness intensity (black andwhite images) or digital colour value. Said preset threshold value orrange for said parameter will depend upon the kind of object that shouldbe detected, which on its turn depends on the kind of histologicaltissue, how it is stained, etc. or on whether the image is ablack-and-white image (such as a radiography) or a coloured image of anykind and origin. Selection of such threshold values or ranges can bemade in any case by the skilled man, for the particular case, withoutexcercize of any inventive skill. For example, if the object whose imagehas to be acquired is a collagen material stained with Sirius Red, theactive pixels may be those having digital values for red and bluebetween 0 and 255 and digital values for green between 0 and 130.

Once the digital image has been quantized to 1 bit, the method of theinvention provides for a stage of metrical processing of the image whichis made on its turn of different stages that will be depicted hereinbelow.

The next stage of the invention method is thus the stage of object'smetrical quantification (QUANT stage).

This stage has been set up for improving metrical quantification of themorphometric parameters of irregularly shaped objects, that can not bemetered by the usual Euclidean geometry. The microscopic observation ofeither a normal or abnormal, such as pathological, component of tissuesamples taken from any organ, particularly liver, is amazing because ofthe new irregularities that appear at any magnification (scale ofobservation). As the extension form of the image of the samples changes,the new irregular details are given measures and dimensions that areindependent at each magnification and can not be arranged in a singlelinear system. Because of this characteristic, which is due to thescabrousness of the external surface of the object to be observed, thevisible details, as well as those that can not be visually identified,make all objects with an irregular surface (such as hepatic tissuesamples) hardly measurable by means of traditional computer-aidedmorphometry.

The classical morphometry tackles the problem of measuring naturalobjects by approximating their irregular outlines and rough surfaces torectilinear outlines and plane surfaces. In addition, there is the wellknown non-representative nature of a bioptic sample as its small volumemakes the so-called disease staging hardly indicative because of theunevenness of the distribution of lesions in the organ as a whole. It isknown that only a slight difference in the site from which a biopticfragment is taken is often sufficient to obtain a sample that indicatesa completely different stage from the one of the adjacent tissue.

Irregular objects were defined “fractal” by Benoit Mandelbrot since, inspite of the fact that their shape changes as a function ofmagnification, they retain the features of their irregularity at allspatial scales. Although the pieces (not fractions) into which they canbe divided are not equal, they preserve the similitude of theirirregularity. This property of the parts into which irregular objectscan be divided is called “self-similarity”. Since the shape of suchobjects depends on the magnification at which their image is observed,any quantitative metering of the dimensions of the object is a functionof the magnification scale. The fractal dimension indicates thereforethe “self-similarity” of the fractal pieces of an irregular body and, ateach scale, defines the characteristics of the reference means used tomeasure the physical and geometrical parameters of the observedirregular object.

The first step of the QUANT stage is the calculation of the area of theobject under examination. The unit of measurement may be μm² or pixel,taking into account that 1.9 pixel side=1 μm (i.e. 1 pixel side=0.526 μmat a 200× magnification and a videocamera resolution of 1.3 Megapixels).The area A of the object under examination is thus calculated bycounting the number of pixels belonging to the cluster of active pixelsselected according to the previous IMA-EL stage.

The second step of the QUANT stage is the calculation of the perimeter Pof the object under investigation. This step is performed by i)selecting the object contour's pixels, and ii) applying to such selectedpixels the perimeter calculation's algorithm according to S. Prashkermethod (Steve Prashker, An Improved Algorithm for Calculating thePerimeter and Area of Raster Polygons, GeoComputation, 1999, which isherein incorporated by reference). According to the Prashker's method,each active pixel's surroundings are taken into consideration, i.e. theeight pixels around the pixel under examination. To each active pixel isgiven a “perimeter value”, whose sum is the overall perimeter P of theobject. If, for example, an internal pixel is considered (i.e. a pixeltotally surrounded by active pixels, thus not belonging to the perimeterof the object), to such a pixel is given a “perimeter value” of 0. If aperimeter's pixel is connected with two other pixels through the cornersalong a diagonal line, the “perimeter value” is {square root}{squareroot over (2)} pixels. If the considered active pixel is connected toone pixel through the corner and to another pixel by a side, the“perimeter value” will be (0.5+{square root}{square root over (2)}/2)pixels. If an active pixel is connected to the two adjacent pixelsthrough its sides, the “perimeter value” will be then 1 pixel and so on.

Given the considerable irregularity of the perimeter of the object underexamination and in order to be able to meter it in concrete terms, anevaluation of its fractal dimension D_(P) is made. Similarly, theestimate of the fractal dimension of the area of the selected collagenicstructure is indicated by the symbol D_(A). Both of these fractaldimensions can be automatically determined using the known“box-counting” algorithm.

According to the “box-counting” method, the fractal dimension D is givenby the mathematical formulaD=lim(ε->0) [logN(ε)/log(1/ε)]wherein ε is the length of the side of the boxes of the grid in whichthe object's image has been divided and N(ε) is the number of boxesnecessary to completely cover the outline (D_(P)) or the area (D_(A)),respectively, of the measured object. The length ε is expressed in pixelor μm and, in the present calculation method, ε tends to 1 pixel.

The next stage of the invention method is thus the stage of dimensionalcalculation (DIM-CLC stage).

In order to avoid difficulties in such a calculation, the fractaldimensions D_(P) and D_(A) are approximated as the slope of the straightline obtained by putting in a Cartesian axis system the parameterslogN(ε) versus log(1/ε).

In practice, the method used to determine D_(P) comprises the followingsteps, performed by the CPU of the processing system 7:

a) dividing the image of the object into a plurality of grids of boxeshaving a side length ε, in which ε varies from a first valuesubstantially corresponding to the side of the box in which said objectis inscribed and a predefined value which is a fraction of said firstvalue,

b) calculating a value of a logarithmic function of N(ε), in which N(ε)is the number of boxes necessary to completely cover the perimeter (P)of the object and of a logarithic function of 1/68 for each ε value ofstep a), thus obtaining a first set of values for said logarithmicfunction of N(ε) and a second set of values for said logarithmicfunction of 1/ε,

c) calculating the fractal dimension D_(P) as the slope of the straightline interpolating said first set of values versus said second set ofvalues of step b).

The same method is applied for calculating the fractal dimension D_(A),with the only difference that, in this case, N(ε) is the number of boxesof side ε that completely cover the area of the object to be quantified.

The next step is the

d) calculation of the corrected perimeter Pf according to the followingalgorithm:Pf=P·[1+λ_(P)(D_(P) −D)]  (I)wherein Pf is the fractal-corrected perimeter, P is the Euclideanperimeter, D_(P) is the fractal dimension, D is the Euclidean dimension(for the perimeter D=1) and λ_(P) is the dilation coefficient. The valueof λ_(P) is empirically determined using a histological section acquiredat different magnifications (5×, 10×, 20×, 40× objective magnification)and then observing new emerging details of the object under evaluation.The λ_(P) is found to be approximately 4.5.

Analogously, Af, i.e. the corrected area of the irregular object to beobserved, is calculated by the CPU of the processing system 7 by meansof the following algorithm:Af=A+[λ _(A)(D _(A) −D)]·(Ap−A)   (Ia)wherein A is the Euclidean area, D is the Euclidean dimension (D=2),λ_(A) is the dilation coefficient which was found to be approximately0.1, Ap the area of the region including the objects to be quantified,automatically obtained with a specific colour threshold and in the samemanner as morphological area A; D_(A) is the fractal dimension of thearea which is calculated by means of the box-counting method.

If the object to be quantified is comprised of a plurality of smallobjects, the calculation of Af can become difficult, in particular forobjects having diameter less than 16 microns. In such a case, thecalculation of the area of such small objects is made by standardmorphometrical evaluation on the active pixels selected according to theIMA-EL stage above described, i.e. by counting the number of activepixels belonging to the same region. To do so, the active pixelsbelonging to a same region, i.e. to the same small object, must be firstof all identified, then each region's area is calculated. Therefore, themethod of the invention comprises a stage of object's sorting (SORTstage) which includes the following steps:

1f) scanning of the image quantized to “1 bit” along a predefineddirection on a x, y axis system;

2f) selecting a first active pixel along said direction of scanning,said active pixel being identified by a first set of x, y values, saidfirst active pixel belonging to a first object's image;

3f) performing on said first selected active pixel a search routine inthe positions next to said selected pixel on the direction's line;

4f) iterating step 3f) until an inactive pixel is found;

5f) assigning to each active pixel selected according to such steps 3f)and 4f) a set of x, y values, saving them in the storing means of theprocessing system 7 (all of such pixels will have the same y value and xvalues in progressive order) and switching said pixels from active toinactive in the object's image;

6f) evaluating for each pixel selected according to steps 3f), 4f) and5f) the two next pixels in the direction ortogonal to the said scanningdirection and selecting the active pixels;

7f) performing, for each of said active pixels selected according tostep 6f), the routine of steps 3f) to 5f);

-   -   8f) iterating steps 6f) and 7f) until all of the connected        pixels belonging to the same object have been saved;

9f) repeating steps 1f) and 2f) until a first active pixel of a furtherobject's image is found;

10f) repeating steps 3f) to 9f) until the whole image has been scanned.

Said predefined direction in step 1f) is preferably from left to rightstarting from top to bottom.

The procedure depicted in steps 1f) to 10f) above allows to identifyobjects made up from 4-connected pixels, i.e. wherein the pixels haveone side in common.

For sorting also 8-connected pixel objects, step 6f) of the aboveprocedure is modified as follows:

6f) evaluating for each pixel selected according to steps 3f), 4f) and5f) the two next pixels in the direction ortogonal to the said scanningdirection and the two pixels adjacent to each of these latter pixels onthe parallel line adjacent to the direction's line and selecting theactive pixels.

The procedure is then prosecuted according to steps 7f) to 10f).

The procedure herein above depicted is a semi-recursive method whichallows, with respect to the standard recursive methods of the art,shorter execution time and less memory request. In fact, taking intoconsideration an image made up of N×M active pixels, only M recursivecalls are necessary, while according to the prior art methods the numberof recursive calls would be N×M−1.

After the SORT stage, the method of the invention may perform thefollowing steps:

1g) calculating the area of each object identified according to the SORTstage by counting the number of pixels belonging to said object's imageand multiplying it for the area of each pixel (1 pixel side=0.526 μm at200× magnification and a video-camera resolution of 1.3 Megapixels);

2g) counting the number of objects and calculating its density.

From what has been said above, it is clear that the calculation methodof the invention represents an improvement if compared with the knownmethods. The fractal geometry offers mathematical models derived fromthe infinitesimal calculus that, when applied to Euclidean geometry,integrate the figures of the morphometrical measurements of natural andirregular objects, thus making them closer to the actual values.

Even if the above described method is construed for the examination of atissue specimen by means of a microscope, it is clear that it can alsobe applied, as said before, to images of the human or animal body orparts thereof, such as for example radiography images, ComputerizedAxial Tomography (CAT), ecography analysis and the like. In such casesuse of the microscope will not be necessary, since the image can bedirectly digitalised by a videocamera and acquired by the computersoftware. Substantially the same stages of the method can therefore beapplied also for such images, the only difference being the fact thatthe image acquisition means 6 read the image directly withoutinterposition of a microscope.

In such cases, where identification of small objects or of objectshaving blurred contour (such as radiographies) is required, the ID stageas described above does not allow an efficient identification, so thatdifferent methods should be used.

Possible procedures of object's identification make use of an imagerepresentation method called Quad Tree. According to such a knownmethod, the image is firstly divided into four quadrants. Each quadrantis on its turn divided into four sub-quadrants and so on up to reachingquadrants of 1 pixel's side. The image information is reported onto atree of degree 4, wherein the parent node contains information which isin common with all of the son nodes (from each parent node originatesfour son nodes) which refer to the four quadrants into which the parentquadrant is divided.

A first alternative identification procedure suitable for the method ofthe invention is an image subtraction technics which comprises thefollowing steps:

1h) generating a blurred image of the object to be examined;

2h) subtracting from the image of the object said blurred image in orderto obtain an image in which the bright colour regions correspond to theimage regions having higher contrast and the dark coloured regionscorrespond to the image regions having lower contrast;

3h) saving in the storing means of the processing system 7 the image ofthe regions whose colour or brightness values are above a predefinedthreshold value.

Preferably, said step 1h) of generating a blurred image is performed by:

dividing the image into quadrants iteratively according to the Quad Treemethod up to quadrant having predefined side length (preferably, 1pixel's side quadrants);

calculating for each quadrant at each division scale the mean value ofthe pixels, in order to associate to each quadrant a set of values;

generating a colour map (RGB images) or an intensity map (grey scaleimages) wherein each point value is the mean of the set of values ofeach quadrant, said colour or intensity map being the blurred image ofthe original image.

The procedure described herein above is particulary suitable in the caseof small objects' detection or to distinguish objects in the foregroundfrom the background.

Furthermore, by modulating the blurring degree, it is possible todiscriminate between objects of different dimension, for example byselecting only objects below a predetermined magnitude. In fact, if theQuad Tree procedure is stopped once a minimum quadrant magnitude of forexample 10 pixel is reached (instead of a minimum 1 pixel magnitude),the blurring degree is higher, which means that a more blurred image isobtained. If such a more blurred image is subtracted from the object'simage according to step 2h) above, all of the objects having a magnitudeabove 10 pixels are excluded and the resulting image shows just thesmaller objects.

A second alternative identification procedure suitable for the ID stageof the method of the invention comprises generating a homogeneity mapaccording to the following steps:

1l) dividing the image into quadrants iteratively according to the QuadTree method up to quadrant having predefined side length (preferably, 1pixel's side quadrants);

21) calculating for each quadrant at each division scale the relativedispersion (RD) obtained as the Standard Deviation divided by the meanvalue of the pixels, in order to associate to each quadrant a set ofvalues of RD;

3l) generating a homogeneity map as a grey scale image, each point'sbrightness being given by the mean of the set of values of RD for eachquadrant, wherein the image's regions having high brightness correspondto homogeneous regions;

4l) selecting the pixels of the homogeneity map having a brightnessintensity above a predefined threshold value and saving their positionin the storing means of the processing system 7.

Naturally, only some specific embodiments of the method and apparatusfor analyizing biological tissue specimens acording to the presentinvention have been described and a person skilled in the art will beable to apply any modification necessary to adapt it to particularapplications without, however, departing from the scope of protection ofthe present invention.

1. Method for processing images of irregularly shaped objects,comprising a stage of acquisition of a digital image of said object, astage of image elaboration (IMA-EL) for quantizing said digital image to1 bit and a stage of metrical processing of said 1-bit quantized image,wherein said stage of metrical processing comprises a stage of object'smetrical quantification (QUANT) for determining Euclidean perimeter Pand/or area A of said object; a stage of dimensional calculation(DIM-CLC) for calculating a fractal-corrected perimeter Pf and/or areaAf of said object, wherein said fractal-corrected perimeter Pf iscalculated from said Euclidean perimeter P and from a fractal dimensionD_(P) of the perimeter; said fractal-corrected area Af is calculatedfrom said Euclidean area A and from a fractal dimension D_(A) of thearea.
 2. A method according to claim 1, wherein said fractal-correctedperimeter Pf is calculated substantally according to the followingformula (I):Pf=P·[1+λ_(P)(D _(P)−1)]  (I) wherein P is the Euclidean perimeter,D_(P) is the fractal dimension of the perimeter and the dilationcoefficient λ_(P) is approximately 4.5.
 3. A method according to claim 1or claim 2, wherein said fractal-corrected area Af is calculatedsubstantally according to the following formula (Ia):Af=A+[λ _(A)(D _(A)−2)]·(Ap−A)   (Ia) wherein A is the Euclidean area,the dilation coefficient λ_(A) is approximately 0.1, Ap is the area ofthe region including the objects to be quantified and D_(A) is thefractal dimension of the area.
 4. A method according to any one ofclaims 1 to 3, wherein said fractal dimension of the perimeter D_(P) andsaid fractal dimension of the area D_(A) are determined according to thefollowing steps: a) dividing the image of the object into a plurality ofgrids of boxes having a side length ε, in which ε varies from a firstvalue substantially corresponding to the side of the box in which saidobject is inscribed and a predefined value which is a fraction of saidfirst value, b) calculating a value of a logarithmic function of N(ε),in which N(ε) is the number of boxes necessary to completely cover theperimeter (P) or the area (A), respectively, of the object and of alogarithmic function of 1/ε for each ε value of step a), thus obtaininga first set of values for said logarithmic function of N(ε) and a secondset of values for said logarithmic function of 1/ε, c) calculating thefractal dimensions D_(P) or D_(A) as the slope of the straight lineinterpolating said first set of values for said logarithmic function ofN(ε) for the perimeter (P) or the area (A), respectively, versus saidsecond set of values of step b).
 5. A method according to any one ofclaims 1 to 4, wherein said stage of image elaboration (IMA-EL) isperformed according to the following steps: 1e) considering a parameterfor each pixel; 2e) comparing said pixel's parameter with a presetthreshold value or threshold range for said parameter; 3e) selecting acluster of active pixels and a cluster of inactive pixels on the base ofsaid comparison.
 6. A method according to claim 5, wherein said pixel'sparameter is brightness intensity (black and white images) or digitalcolour value.
 7. A method according to any one of claims from 1 to 6,wherein in said stage of object's metrical quantification (QUANT) thearea A of the object is calculated by counting the number of pixelsbelonging to the cluster of active pixels selected according to theprevious IMA-EL stage and correlating said number of counted activepixels with the area A of the object.
 8. A method according to any oneof claims from 1 to 7, wherein in said stage of object's metricalquantification (QUANT) the perimeter P of the object is calculated by i)selecting the object contour's pixels, and ii) applying to such selectedpixels a perimeter calculation's algorithm.
 9. A method according toclaim 8, wherein said perimeter calculation's algorithm is according toS. Prashker method, wherein to each active pixel belonging to the objectis given a “perimeter value”, which is a function of the position of theactive pixels adjacent to the pixel under examination, the sum of said“perimeter values” being the overall perimeter P of the object.
 10. Amethod according to any one of claims from 1 to 9, further comprising astage of object's sorting (SORT) for identifying objects made up from4-connected pixels, which includes the following steps: 1f) scanning ofthe image quantized to “1 bit” along a predefined direction on a x, yaxis system; 2f) selecting a first active pixel along said direction ofscanning, said active pixel being identified by a first set of x, yvalues, said first active pixel belonging to a first object's image; 3f)performing on said first selected active pixel a search routine in thepositions next to said selected pixel on the direction's line; 4f)iterating step 3f) until an inactive pixel is found; 5f) assigning toeach active pixel selected according to such steps 3f) and 4f) a set ofx, y values, saving them in the storing means of the processing system 7and switching said pixels from active to inactive in the object's image;6f) evaluating for each pixel selected according to steps 3f), 4f) and5f) the two next pixels in the direction ortogonal to the said scanningdirection and selecting the active pixels; 7f) performing, for each ofsaid active pixels selected according to step 6f), the routine of steps3f) to 5f); 8f) iterating steps 6f) and 7f) until all of the connectedpixels belonging to the same object have been saved; 9f) repeating steps1f) and 2f) until a first active pixel of a further object's image isfound; 10f) repeating steps 3f) to 9f) until the whole image has beenscanned.
 11. A method according to claim 10, wherein said predefineddirection in step 1f) is preferably from left to right starting from topto bottom.
 12. A method according to any one of claims from 1 to 11,wherein the stage of object's sorting according to steps 1f) to 10f) ofclaim 8 is performed for also identifying objects made up from8-connected pixels, in said stage the step 6f) being modified asfollows: 6f) evaluating for each pixel selected according to steps 3f),4f) and 5f) the two next pixels in the direction ortogonal to the saidscanning direction and the two pixels adjacent to each of these latterpixels on the parallel line adjacent to the direction's line andselecting the active pixels.
 13. A method according to any one of claimsfrom 10 to 12, further comprising the following steps: 1g) calculatingthe area of each object identified according to the SORT stage bycounting the number of pixels belonging to said object's image andcorrelating it with the area of said each object; 2g) optionally,counting the number of objects and calculating its density.
 14. A methodaccording to any one of claims from 1 to 13, wherein said stage ofacquisition of the digital image of the object comprises the following:-) stage of providing a system (1) for acquiring and processing an imageincluding a microscope (2) having a motorised scanning stage (3) capableof moving along the Cartesian axis x, y, z, electronic image acquisitionmeans (6) operatively connected to said microscope (2), said motorisedscanning stage (3) and said electronic image acquisition means (6) beingoperatively connected to a processing system (7), said processing system(7) comprising a processing unit (CPU), storing means which include aRAM working memory and a hard disk; -) stage of identification of theobject (ID) for saving the Cartesian parameters of the object's image,and -) stage of image acquisition (IM-ACQ) of said identified object.15. A method according to claim 14, wherein said stage of identificationof the object (ID) comprises the following steps: 1a) generating, at apreset magnification of the said microscope (2), a grid formed by aplurality of boxes to overlap on the image; 2a) sending a command to themotorized scanning stage (3) to position on the axis x, y in a firstposition (start position) corresponding to the alignment position of themicroscope's object glass with a first box of the grid whose image hasto be acquired and a command to the electronic image acquisition means(6) for acquiring the digital image for such a first box, the imagebeing temporarily saved in the working memory (RAM); 3a) evaluating thebrightness of the single pixels in the first box, comparing it with apreset value and determining the brightness contrast inside the box; 4a)sending a command to the motorized scanning stage (3) to position on theaxis x, y in a next position (second position) corresponding to a secondbox of the grid, sending a command to the electronic image acquisitionmeans (6) for acquiring and temporarily saving on the working memory(RAM) the digital image for such a second box and repeating theoperations of step 3) on such image; 5a) reiterating the routine of step4) until the whole slide is scanned and the images for each box of thegrid are processed, wherein during the execution of the whole routine,the x, y position of the boxes of the grid having a brightness contrastabove a predefined value are saved in the hard-disk memory, wherein saidpreset magnification of the said microscope (2) is preferably 200×magnification.
 16. A method according to claim 15, wherein said step 3a)of processing the image of the box of the grid which has beentemporarily saved in the working memory (RAM) is performed according tothe following method: i) building a histogram of the brightnessintensities of the pixels of the analysed box, ii) calculating thestandard deviation from the mean value of the histogram, and iii)comparing the calculated value of standard deviation with a predefinedvalue, wherein the position of the boxes having a standard deviationabove such a predefined value is saved on the hard disk.
 17. A methodaccording to any one of claims from 1 to 16, wherein said ID stage isreplaced by or preceded by an identification preview stage (ID-PREV),which comprises steps from 1a) to 5a) as depicted in claim 13 andwherein said preset magnification of the microscope (2) is selectedbetween 25× and 100×.
 18. A method according to any one of claims from14 to 17, further comprising a stage of focus setting (FCS) whichincludes the following steps: 1b) selecting a plurality of focus pointson the object image to be acquired; 2b) sending a command to thescanning stage (3) to position the first focus point below themicroscope's object glass; 3b) manually bringing into focus said firstfocus point and automatically saving its focus parameters in the storingmeans of the processing system (7); 4b) repeating the routine of steps2b) and 3b) for each point.
 19. A method according to claim 18, whereinsaid selected focus points are equally spaced apart from each other orhomogenously distributed on the object's surface.
 20. A method accordingto claim 19, wherein nine focus points are selected and are located atthe four corners of the largest parallelepiped inscribed into the objectunder examination, at the center thereof and at the median point of theparallelepiped's sides.
 21. A method according to any one of claims from14 to 20, further comprising a stage of white calibration (WCAL), whichcomprises 1c) acquiring through the electronic image acquisition means(6) the image of a specimen-free region (blank image) and saving it inthe storing means of the processing system (7).
 22. A method accordingto any one of claims from 18 to 21, wherein said stage of imageacquisition (IM-ACQ) comprises the following steps: 1) sending a commandto the scanning stage (3) in order to move it to a first saved box′position of the grid, selected according to previous step 5a) accordingto claim 13, in alignment with the microscope's object glass; 2d)calculating the focus parameters for said first box image byinterpolation from the focus parameters calculated according to previoussteps 1b) to 4b) for at least two focus points proximal to the saidfirst box; 3d) acquiring the image of said first box through said imageacquisition means (6); 4d) subtracting from the acquired image of saidfirst box the blank image acquired according to step 1c) above; 5d)saving the image resulting from step 4d) in the storing means of theprocessing system (7); 6d) repeating steps from 1d) to 5d) until thewhole object to be acquired has been scanned; 7d) reassembling the wholeimage of the object by aligning the images of the single boxes inrelation to their initial position and saving said whole image in thestoring means of the processing system (7).
 23. A method according toclaim 22, wherein said step 7d) of reassembling the whole image of theobject comprises: 1) aligning each box′ image with the adjacent box′image by overlapping the edges of the image's side in the direction ofalignment; m) in the region of overlap, minimizing the difference ofbrightness and/or colour intensity between overlapping pixels byshifting the box′ images one with respect to each other; n) repeatingsteps l) and m) for each pair of adjacent boxes.
 24. A method accordingto any one of claims from 14 to 23, wherein said ID stage is performedby: 1h) generating a blurred image of the object to be examined; 2h)subtracting from the image of the object said blurred image in order toobtain an image in which the bright colour regions correspond to theimage regions having higher contrast and the dark coloured regionscorrespond to the image regions having lower contrast; 3h) saving in thestoring means of the processing system (7) the image of the regionswhose colour or brightness values are above a predefined thresholdvalue.
 25. A method according to claim 24, wherein said step 1h) ofgenerating a blurred image comprises: -) dividing the image intoquadrants iteratively according to the Quad Tree method up to quadranthaving predefined side length; -) calculating for each quadrant at eachdivision scale the mean value of the pixels, in order to associate toeach quadrant a set of values; -) generating a colour map (RGB images)or an intensity map (grey scale images) wherein each point value is themean of the set of values of each quadrant, said colour or intensity mapbeing the blurred image of the original image.
 26. A method according toany one of claims from 14 to 23, wherein said ID stage is performed by:1l) dividing the image into quadrants iteratively according to the QuadTree method up to quadrant having predefined side length; 2l)calculating for each quadrant at each division scale the relativedispersion (RD) obtained as the Standard Deviation divided by the meanvalue of the pixels, in order to associate to each quadrant a set ofvalues of RD; 3) generating a homogeneity map as a grey scale image,each point's brightness being given by the mean of the set of values ofRD for each quadrant, wherein the image's regions having high brightnesscorrespond to homogeneous regions; 4) selecting the pixels of thehomogeneity map having a brightness intensity above a predefinedthreshold value and saving their position in the storing means of theprocessing system (7).
 27. A method according to any one of claims from14 to 26, wherein said system (1) includes a motorised scanning stage(3) capable of moving along the Cartesian axis x, y, z, electronic imageacquisition means (6) operatively aligned with said motorised scanningstage (3), said motorised scanning stage (3) and said electronic imageacquisition means (6) being operatively connected to a processing system(7), said processing system (7) comprising a processing unit (CPU),storing means which include a RAM working memory and a hard disk.
 28. Amethod for acquiring the digital image of an object, said methodcomprising the stages as depicted in any one of claims from 14 to 27.29. A system (1) for acquiring and processing an image including amicroscope (2) having a motorised scanning stage (3) capable of movingalong the Cartesian axis x, y, z, electronic image acquisition means (6)operatively connected to said microscope (2), said motorised scanningstage (3) and said electronic image acquisition means (6) beingoperatively connected to a processing system (7), said processing system(7) comprising a processing unit (CPU), storing means which include aRAM working memory and a hard disk, said processing system (7) running aprogram (PRG) to perform a method according to any one of claims from 1to
 28. 30. A system (1) for acquiring and processing an image includinga motorised scanning stage (3) capable of moving along the Cartesianaxis x, y, z, electronic image acquisition means (6) operatively alignedwith said motorised scanning stage (3), said motorised scanning stage(3) and said electronic image acquisition means (6) being operativelyconnected to a processing system (7), said processing system (7)comprising a processing unit (CPU), storing means which include a RAMworking memory and a hard disk, said processing system (7) running aprogram (PRG) to perform a method according to any one of claims from 1to
 28. 31. A software program (PRG) to perform the method according toany one of claims from 1 to
 28. 32. A computer readable supportcomprising a program (PRG) to perform the method according to any one ofclaims from 1 to
 28. 33. Use of a system (1) according to claim 29 orclaim 30, for performing a method as depicted in any one of claims from1 to 26.